背景:及时有效地识别患有抑郁症(DS)的个体对于提供及时治疗至关重要。机器学习模型在这一领域表现出了希望;然而,研究往往不足以证明使用这些模型的实际好处,并且无法提供切实的实际应用。
目的:本研究旨在建立一种新的方法来识别可能表现出DS的个体,通过概率测度以更可解释的方式识别最有影响力的特征,并提出可用于实际应用的工具。
方法:该研究使用了3个数据集:PROACTIVE,2013年巴西国家健康调查(PesquisaNacionaldeSaúde[PNS])和PNS2019,包括社会人口统计学和健康相关特征。使用贝叶斯网络进行特征选择。然后使用选定的特征来训练机器学习模型以预测DS,在9项患者健康问卷中,评分≥10。与随机方法相比,该研究还分析了不同敏感性率对减少筛选访谈的影响。
结果:该方法允许用户在灵敏度之间进行明智的权衡,特异性,减少面试次数。在通过最大化Youden指数确定的阈值0.444、0.412和0.472下,模型的灵敏度为0.717、0.741和0.718,特异性为0.644、0.737和0.766,分别为PNS2013和PNS2019。这3个数据集的接收器工作特性曲线下面积分别为0.736、0.801和0.809,分别。对于PROACTIVE数据集,最具影响力的特征是姿势平衡,呼吸急促,以及老年人的感觉。在PNS2013数据集中,特点是能够进行日常活动,胸痛,睡眠问题,和慢性背部问题。PNS2019数据集与PNS2013数据集共享3个最具影响力的特征。然而,不同的是用言语虐待代替了慢性背部问题。重要的是要注意,PNS数据集中包含的特征与PROACTIVE数据集中的特征不同。实证分析表明,使用所提出的模型可导致筛选访谈减少52%,同时保持0.80的敏感性。
结论:这项研究开发了一种新的方法来识别患有DS的个体,展示了使用贝叶斯网络识别最重要特征的实用性。此外,这种方法有可能大大减少筛选访谈的数量,同时保持高度的敏感性,从而促进改善DS患者的早期识别和干预策略。
BACKGROUND: Identifying individuals with depressive symptomatology (DS) promptly and effectively is of paramount importance for providing timely treatment. Machine learning models have shown promise in this area; however, studies often fall short in demonstrating the practical benefits of using these models and fail to provide tangible real-world applications.
OBJECTIVE: This study aims to establish a novel methodology for identifying individuals likely to exhibit DS, identify the most influential features in a more explainable way via probabilistic measures, and propose tools that can be used in real-world applications.
METHODS: The study used 3 data sets: PROACTIVE, the Brazilian National Health Survey (Pesquisa Nacional de Saúde [PNS]) 2013, and PNS 2019, comprising sociodemographic and health-related features. A Bayesian network was used for feature selection. Selected features were then used to train machine learning models to predict DS, operationalized as a score of ≥10 on the 9-item Patient Health Questionnaire. The study also analyzed the impact of varying sensitivity rates on the reduction of screening interviews compared to a random approach.
RESULTS: The methodology allows the users to make an informed trade-off among sensitivity, specificity, and a reduction in the number of interviews. At the thresholds of 0.444, 0.412, and 0.472, determined by maximizing the Youden index, the models achieved sensitivities of 0.717, 0.741, and 0.718, and specificities of 0.644, 0.737, and 0.766 for PROACTIVE, PNS 2013, and PNS 2019, respectively. The area under the receiver operating characteristic curve was 0.736, 0.801, and 0.809 for these 3 data sets, respectively. For the PROACTIVE data set, the most influential features identified were postural balance, shortness of breath, and how old people feel they are. In the PNS 2013 data set, the features were the ability to do usual activities, chest pain, sleep problems, and chronic back problems. The PNS 2019 data set shared 3 of the most influential features with the PNS 2013 data set. However, the difference was the replacement of chronic back problems with verbal abuse. It is important to note that the features contained in the PNS data sets differ from those found in the PROACTIVE data set. An empirical analysis demonstrated that using the proposed model led to a potential reduction in screening interviews of up to 52% while maintaining a sensitivity of 0.80.
CONCLUSIONS: This study developed a novel methodology for identifying individuals with DS, demonstrating the utility of using Bayesian networks to identify the most significant features. Moreover, this approach has the potential to substantially reduce the number of screening interviews while maintaining high sensitivity, thereby facilitating improved early identification and intervention strategies for individuals experiencing DS.